The overall goal of this research is to improve understanding of nonverbal communication disorders among patients with neurologic disease. Facial expressions are complex signals that are brief, last only a few minutes and important for communicating intention, motivation, and emotional states. In humans a variety of neurologic and psychiatric conditions alter the propensity to use facial signals. In fact, diminished facial expressivity, or the "masked face" is one of the cardinal features of Parkinson's disease, a neurodegenerative basal ganglia disorder that primarily affects older adults. Classically, it has been held that Parkinson's disease represents a "model system" for impairing spontaneous (limbic) facial emotions, whilst leaving intentionally posed (cortical) facial expressions intact. Preliminary data, using computer imaging methods to quantify dynamic movement changes over the face, tentatively suggests that this dissociation may be unfounded (Bowers et al., in press). The purpose of the proposed research is to apply computing imaging methods to learn: (a) whether diminished facial expressivity among Parkinson patients involves modulatory defects that influence both volitional and spontaneous emotions; (b) whether these modulatory defects are related to dopaminergic deficiency, and (c) which parameters of facial expressivity (timing, frequency, entropy) are improved by a behavioral intervention for treating respiratory strength in PD. Taken together, the findings from this study may facilitate understanding of the mechanisms underlying diminished facial expressivity in Parkinson's disease and provide information that may ultimately be useful in the treatment of nonverbal communication disturbances.